Table 2.
Architecture of the CNN.
| Layer | Properties | ||||
|---|---|---|---|---|---|
| Input | 64 × 64 pixels | 3 channels | |||
| Convolutional | 32, 6 × 6 pixel kernels | 1 pixel stride | Same padding | Batch normalisation | ReLU activation |
| Dropout | Dropout rate of 0.2 | ||||
| MaxPooling | 2 × 2 pixel | 2 pixel stride | |||
| Convolutional | 64, 5 × 5 pixel kernels | 1 pixel stride | Same padding | Batch normalisation | ReLU activation |
| Dropout | Dropout rate of 0.2 | ||||
| MaxPooling | 2 × 2 pixel | 2 pixel stride | |||
| Convolutional | 128, 3 × 3 pixel kernels | 1 pixel stride | Same padding | Batch normalisation | ReLU activation |
| Dropout | Dropout rate of 0.2 | ||||
| Convolutional | 128, 3 × 3 pixel kernels | 1 pixel stride | Same padding | Batch normalisation | ReLU activation |
| Dropout | Dropout rate of 0.2 | ||||
| MaxPooling | 2 × 2 pixel | 2 pixel stride | |||
| Flatten | |||||
| Fully connected | 2048 neurons | Batch normalisation | ReLU activation | ||
| Dropout | Dropout rate of 0.2 | ||||
| Fully connected | 2048 neurons | Batch normalisation | ReLU activation | ||
| Dropout | Dropout rate of 0.2 | ||||
| Output | 2 neurons | Softmax activation |
Notes. The first column in the type of layer while the second column contains the associated properties. The input is a 64 × 64 pixel, three channel image and the output is two probabilities, one for the probability the input is a merger and one for the probability the input is a non-merger. Further details on what the properties of the layers mean can be found in Sect. 3.2.
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